Increasing the Accessibility and Acceptability of HIV Counseling and Testing among Aboriginal Women in Ottawa
Bibliographic record
Abstract
Background: In Canada, Aboriginal women are disproportionately impacted by HIV and are tested later in disease progression, resulting in poorer health outcomes and increasing the risk of onward transmission. Methods: Using purposive sampling, 13 self-identified Aboriginal women participated in in-depth, qualitative interviews exploring women’s experiences with HIV testing and their ideas for improving the process. Thematic analysis was conducted in conjunction with constant comparison to identify emergent themes and to direct future interviews and analyses. Results: Women identified several barriers to HIV testing converging on the subjects of insufficient knowledge of HIV and HIV transmission, lack of perceived relevance of HIV testing, unwillingness or inability to confront the need for testing, and judgment from self and others regarding engagement in HIV-related risk-behaviours. The women also described their acceptable and unacceptable testing experiences, presented recommendations for increasing HIV testing uptake, and suggested ways to create the ideal testing experience. The findings demonstrate a clear need for stronger engagement of Aboriginal women surrounding their HIV-related testing needs and increased access to educational opportunities, culturally appropriate care, and initiatives aimed at reducing societal stigma around HIV.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".